As already mentioned, the output of an ML model can
also be probabilistic. In [1]}, the authors trained an ANN using channel statistics (moments and covariances of channel transfer functions) over many simulated realisations of two stochastic propagation models; the path graph (PGr) and the Saleh-Valenzuela (SV) model. The trained ANN was able to generate
valid parameters for the two probabilistic models, so that both showed a similar power density profile to the actual one. Likewise, in [2]}, the authors used a CNN-based approach for PL distribution estimation in a variety of different urban and suburban environments. Satellite images of the environments were first converted to 3D models that were imported into an RT solver. Then, the simulated PL was used to generate the PL probability density function in each environment. In such cases, the number of bins that comprise the distributions should be carefully chosen.
